code: def prepare(self, priming_data, training_data=None):
      self.tfidf_vectorizer = TfidfVectorizer(
          ngram_range=self.ngram_range, max_features=self.max_features
      )
      self.tfidf_vectorizer.fit_transform(
          [str(x) for x in priming_data]
      )
  
file_path: lightwood/lightwood/encoder/text/tfidf.py
imports: import torch
  from sklearn.feature_extraction.text import TfidfVectorizer
  import numpy
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 2 for calling the TfidfEncoder obj's underlying member method code with example usage: obj.prepare(priming_data, training_data) without return types